Spectral imaging perspective on cytomics
Spectral imaging perspective on cytomics
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DOI：
10.1002/cyto.a.20292
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发表时间：
2006-07-01
期刊：
影响因子：
3.7
通讯作者：
Levenson, Richard M.
中科院分区：
文献类型：
--
作者：
Levenson, Richard M.
背景资料：细胞组学涉及细胞形态和分子表型的分析，参考组织结构和额外的元数据。为此，需要整合各种成像和非成像技术。光谱成像被提出作为一种工具，可以简化和丰富的形态和分子信息的提取。可以使用安装在标准显微镜上的简单易用的仪器，并可以生成具有出色空间和光谱分辨率的光谱图像数据集;这些可以通过复杂的分析工具来利用。方法：本报告重点介绍基于明场显微镜的方法。使用非特异性标准染色剂（Giemsa;苏木精和伊红（H&E））或免疫组织化学（IHC）技术，采用三种色原加苏木精复染，对细胞学和组织学样本进行染色。使用Nuance（TM）系统对样品进行成像，Nuance（TM）系统是一种市售的基于液晶可调谐滤波器的多光谱成像平台。由此产生的数据集进行了分析，使用光谱解混算法和/或学习的例子classification tools.Results：光谱解混的Giemsa染色的豚鼠血片很容易分类的主要血液元素。机器学习分类器在同一任务中也取得了成功，并在结肠癌示例中区分正常区域和恶性区域，以及在H& E染色的肾脏样本中描绘炎症区域。在一个多路复用ICH样品的例子中，棕色，红色和蓝色的色原被分离到单独的图像没有串扰或干扰（也蓝色）苏木素counterstain.Conclusion：细胞学需要准确的建筑分割以及多路复用的分子成像相关联的分子表型与相关的细胞和组织舱室。多光谱成像可以帮助这两项任务，并传达新的效用，以明场为基础的显微镜方法。(c)2006年国际分析细胞学学会。
Background: Cytomics involves the analysis of cellular morphology and molecular phenotypes, with reference to tissue architecture and to additional metadata. To this end, a variety of imaging and nonimaging technologies need to be integrated. Spectral imaging is proposed as a tool that can simplify and enrich the extraction of morphological and molecular information. Simple-to-use instrumentation is available that mounts on standard microscopes and can generate spectral image datasets with excellent spatial and spectral resolution; these can be exploited by sophisticated analysis tools.Methods: This report focuses on brightfield microscopy-based approaches. Cytological and histological samples were stained using nonspecific standard stains (Giemsa; hematoxylin and eosin (H&E)) or immunohistochemical (IHC) techniques employing three chromogens plus a hematoxytin counterstain. The samples were imaged using the Nuance (TM) system, a commercially available, liquid-crystal tunable-filter-based multispectral imaging platform. The resulting data sets were analyzed using spectral unmixing algorithms and/or learn-by-example classification tools.Results: Spectral unmixing of Giemsa-stained guinea-pig blood films readily classified the major blood elements. Machine-learning classifiers were also successful at the same task, as well in distinguishing normal from malignant regions in a colon-cancer example, and in delineating regions of inflammation in an H&E-stained kidney sample. In an example of a multiplexed ICH sample, brown, red, and blue chromogens were isolated into separate images without crosstalk or interference from the (also blue) hematoxylin counterstain.Conclusion: Cytomics requires both accurate architectural segmentation as well as multiplexed molecular imaging to associate molecular phenotypes with relevant cellular and tissue compartments. Multispectral imaging can assist in both these tasks, and conveys new utility to brightfield-based microscopy approaches. (c) 2006 International Society for Analytical Cytology.